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, and improve prediction accuracy - develop Machine Learning and AI algorithms for crop management, yield predictions and decision support systems - prepare manuscripts for peer-reviewed publications
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opportunity to choose between two missions: • Mission 1: Improve new automated algorithmic schemes to quickly, efficiently and robustly detect and extract recorded geophysical signals related to earthquakes
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing
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outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision
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software tools for system control, data acquisition, and automation — preferably in NI LabVIEW or equivalent platforms. Build smart algorithms to optimize real-time energy production from various sources and
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using simulation-based parametric studies. Couple computational results with experimental data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large
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utilization. The successful candidate will play a significant role in the EU‑funded TIMBERHAUS project (www.timberhaus.eu). Your tasks Develop machine learning models and computer vision algorithms for wood
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for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section